March 18, 2024 • 19 mins
Welcome to a special edition of The Cloud Collective Podcast, where we explore the transformative world of artificial intelligence (AI) and focus on practical strategies for success.
In this episode, host John Kundtz is joined by Kyndryl Vice President and Distinguished Engineer Greg Pruett, a multi-patent holder and expert in infrastructure and cloud architecture.
Greg begins by clarifying the differences between traditional predictive AI and the emerging field of generative AI, which has the unique ability to create new content.
He then outlines the five crucial steps for getting started with AI:
- Analyze Business Needs and Desired Outcomes: Conduct a structured assessment to identify potential use cases and quantify the return on investment from AI adoption.
- Define Your Data Strategy: AI systems are only as good as the data they're trained on. Greg emphasizes the importance of data quality, inclusivity, and representativeness and addresses privacy, security, and bias concerns.
- Choose an AI Software Platform: Explore the growing ecosystem of AI platforms that offer pre-trained models and accelerate adoption by enabling organizations to use rather than invent AI solutions.
- Select AI Infrastructure: Decide whether to run AI workloads on public cloud, private cloud, or a hybrid model, considering factors like data sovereignty and compliance requirements.
- Establish ModelOps Processes: Implement robust processes for monitoring, updating, and testing AI models to ensure responsible deployment and maintain high-quality outputs over time.
The show wraps up with insights into overcoming common challenges in AI projects, ensuring data quality, and operationalizing AI within your organization.
Want to dive deeper into the world of AI and explore how it can revolutionize your business?
Connect directly with Greg Pruett on LinkedIn and check out his insightful article, "5 Steps to Get Started with AI," for more valuable advice.
Don’t forget to subscribe to this podcast on your favorite podcast platform to stay updated with the latest insights from industry leaders.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
John Kundtz (00:00):
AI Insights with Greg Pruitt a five-step
blueprint for success.
Hi everybody, my name is JohnKuntz and welcome to this
special edition of the CloudCollective podcast.
(00:21):
In today's episode, I amexcited to welcome back Kindrel
Vice President, distinguishedengineer, multiple patent holder
and infrastructure cloudarchitect, greg Pruitt, as he
shares his valuable advice onhow to get started with
artificial intelligence.
If you are looking for help toframe your thinking around AI in
(00:42):
your AI projects, then you havecome to the right place.
I want to welcome back GregPruitt.
Hi, john, thanks for having meback on the show.
Hey, it's great to see youagain.
For those that weren't aroundor would like a refresh, why
don't you share a little bitabout your background with our
listeners and just feel free tostart anywhere you want?
Greg Pruett (01:07):
Thanks, john, I appreciate it.
My background is actuallyprimarily in system design.
I spent many years designingsystems for IBM and Lenovo, so I
have a deep engineeringbackground as well as a software
engineering background.
More recently, I've been partof Kindrel working on services
(01:33):
and helping our customers toadopt new technology, so I'm
part of the CTO office here inKindrel and recently doing some
very exciting work withartificial intelligence.
John Kundtz (01:46):
That's spectacular.
I'll tell you what.
Every time I get to talk to anyof our distinguished engineers,
it's always a fascinating topicand always learn a lot.
You can't go anywhere todaywithout having something in the
news about artificialintelligence, or AI.
There's a ton of hype around it, particularly around gen AI or
(02:07):
generative AI or chat GPT, butAI has been around a pretty long
time and before this hypearound generative AI came along,
there was predictive AI, whichis sometimes referred to as
traditional AI.
I wonder if you could justspend a moment, greg, explaining
the differences and maybe givesome examples of each.
Greg Pruett (02:29):
That's a great point, John.
Artificial intelligence isaround in a lot of different
forms, and generative AI is justone field of artificial
intelligence.
But there are numerous otherplaces where artificial
intelligence has made an impacton our lives.
You think about naturallanguage processing, Think about
(02:55):
your home devices, your Google,your Alexa, your Siri.
Those are devices that canunderstand speech and respond to
speech requests, typicallyusing something like a recurrent
(03:16):
neural network in R&M.
Also tremendous advances in thearea of computer vision over
the years typicallyconvolutional neural network,
CNNs that are very good atrecognizing objects or being
(03:37):
able to recognize objects invideo, used in transportation,
for self-driving vehicles, usedin retail for self-checkout or
loss prevention.
A lot of these, what you callmore traditional AI technologies
(04:01):
, are also maturing and becomingmuch more widely used in
production environments.
It's not just about looking atgenerative AI.
We're helping our clients withall types of AI.
Now, generative AI, like youwere saying, is quite different.
(04:22):
While it's based on pre-traineddata, generative AI actually
has this unique ability togenerate or create new content.
It provides some I'll sayrandomization to the pre-trained
data and allows it to createsummaries of documents, write
(04:49):
Q&As based on a large volume ofdata that actually create blogs
or even presentations.
Now we're seeing some excitingnew areas of generative AI
creating pictures and evengenerating videos.
John Kundtz (05:08):
Greg, many companies are reading about AI,
especially large enterprises.
They're trying to figure outhow to leverage some of these
technologies to innovate, toimprove productivity, to help
their decision-making, gaincompetitive advantage, reduce
time to market, stuff like that.
(05:30):
But I think a lot of newtechnologies people struggle on
how do you get started?
Greg Pruett (05:38):
With AI.
I think there's multiple stepsto it.
Maybe I can organize thatanswer into a few steps.
First, like you were saying,there are lots of ways that you
could apply AI, but we reallyencourage our customers to do a
(06:03):
very structured assessment sothat they can look at different
business needs, differentdesired outcomes, and go through
and score what kind of returnon investment they could get out
of these different scenarios.
I really think it starts withbusiness needs and how you can
(06:29):
achieve business differentiation.
Then, I would say, probably thesecond most important thing is
thinking about true data andyour data strategy.
Ai can do amazing things interms of consolidating data or
(06:52):
producing insights out of dataor making predictions from data,
but the whole system is only asgood as the data.
So most of us need to spendconsiderable time thinking about
data science and thinking aboutwhat kind of data quality, data
(07:15):
cleanup, data reliability needsto be implemented, as the data
sources are certainly veryimportant, but along with that
and last time, of course, john,we talked about sovereign cloud
Another very important thingthat we're seeing more and more
(07:36):
in the news these days is, youknow, making sure you deal with
anonymizing data, keeping dataprivate and ensuring fairness
and avoiding bias and all ofthose things.
John Kundtz (07:57):
So there's a lot of aspects to your data strategy
in terms of making sure it's agood data set, it's an inclusive
data set, it's a representativedata set, and so that's another
very important aspect of, Iwould say, preparing and that's
probably a key step, right, ifyou think about it, if you think
(08:22):
about these large languagemodels, and you don't want to
train them on somebody else'sdata, and you certainly don't
want to expose your data tosomebody outside of your
organization, particularly ifyou're trying to build a
competitive advantage or dosomething innovative.
It's the garbage in garbage.
(08:42):
Have to sort up your data, yourstrategy, where it is, who has
access to it, who doesn't haveaccess to it, before you can
actually, I think, really dosomething unique within your
organization, particularly alarge enterprise.
Does that make sense?
Greg Pruett (09:01):
Yeah and John, I joke with people sometimes that
the biggest winners in AI mayactually be the lawyers.
We're starting to see more andmore public lawsuits against AI
companies, either for trainingdata that may have included
(09:24):
trademark material, or due tobias and fairness or openness.
We're seeing various types oflawsuits now, so that data
strategy is really key.
What are your data sources?
What are your privacy controls,your responsibility controls?
(09:47):
Cool, the number three.
In the first two you notice Ididn't really mention any AI
terms like creating models orany of that.
I was just talking about basicsbusiness needs, return on
investment and data sources andprivacy.
(10:09):
I would say after that, you dowant to think about what type of
AI, like you're asking, isgenerative AI correct or are
there other AI technologies thatcan help solve that problem?
One thing I normally recommendis to think carefully about
(10:32):
adopting what I call an AIsoftware platform.
There's a lot of very good AIplatforms emerging.
Of course, there's the GoogleVertex platform, the NVIDIA AI
Enterprise platform.
There's Red Hat OpenShift AIplatform.
(10:52):
There's a number of very goodsoftware platforms that are
emerging that can really helpyou adopt AI more quickly in
terms of using it, not inventingit.
These AI platforms can help youby providing you pre-trained
models.
(11:13):
Maybe you don't even need to gothrough the training step, or
maybe you do, but maybe you canstart with a pre-trained model
that's pretty good and then dosome customizing or tuning on
that model with your ownspecific date.
Then after that, after youunderstand and pick an AI
(11:36):
software platform, then it'swhen I would start thinking
about infrastructure.
Where do you want to run this?
Where do you want to run thisin terms of a production
environment?
Do you want to run it in publiccloud?
Do you have data sovereigntyrequirements that make you want
to run it in private cloud?
(11:56):
All of those are possible.
You're going to have to makedecisions, choosing a software
platform and choosing a privateor public infrastructure for
hosting your production.
Ai, I think, or steps three.
John Kundtz (12:15):
That makes sense, like she's describing.
Your first two steps arebasically I would call it basic
business strategy blocking andtackling.
Now you're ready to put thefoundation.
Now you're ready to build thewalls.
You've got to pick yourplatform and then figure out
where you want to store yourdata and run the software.
(12:37):
Now we've got the first fourdown.
I believe there's one more stepthat you recommend.
Greg Pruett (12:47):
So for the fifth step, I think you really have to
think about the operationalmodel, the process for running a
production AI environment, andmaybe this isn't as intuitive
Like any other softwareapplication.
(13:07):
You will need to think aboutthe lifecycle of that
application.
How do you update the contentin the model?
Maybe if you have a generativeAI model that's trained on all
the user manuals for automobiles, what if a new generation of
(13:29):
automobiles comes out, a newmodel years?
You need to be able to updatethe model and add more data.
Whenever you update a model,though, there's a number of
things that should be done.
We talked about, with the datastrategy, making sure that your
data is clean and accurate andrepresentative, but also your
(13:56):
model needs to be tested to seeif there's bias, to see if it
produces bad results, and soanytime you retrain, you want to
retest and look at thoseresponsible AI processes to
(14:18):
ensure that what you're puttingout is of high quality.
John Kundtz (14:26):
Makes sense.
If you look at some of thegenerative AI companies like
OpenAI or Claude from Anthropicsif you just pick on OpenAI
GPT-4, it was around for almostsix or eight months before they
let it loose into the world.
I believe, from what I've read,they spent a lot of time on
(14:48):
security, on accuracy, onprivacy and bias and all those
things you just talked about.
Hit the market like with a bang.
I think you're right.
The operational processes aregoing to be your guardrails.
How is it used?
We learned of anything is youcan't just take everything right
(15:09):
out the gate as gospel truthright, at least from my
experience with generative AI.
It's another smart person inthe room I normally look forward
to that fifth step, as isModelOps or Machine LearningOps,
modelops Basically the fivesteps and, by the way, this is
based on your blog posts thatyou wrote and will obviously
(15:31):
include the link in the shownotes to it, the summary.
You analyze your business needs, your desired outcomes, the
requirements.
You define your data strategy,pick an AI software platform and
then you select how you'regoing to run the infrastructure
public cloud versus privatecloud and then you establish
(15:52):
your ModelOps processes.
That is, I think, excellent andvery valuable information.
Any concluding thoughts orparting words of wisdom on this
topic?
Greg Pruett (16:07):
I guess I'll say two things.
One I hope I didn't make itsound too daunting.
I think there's a fairly simpleformula and we have a process
that we're doing with ourclients that's repeatable and
ensuring successful AI projects.
The second thing, though, I'llsay is that there's some studies
(16:31):
out there that say that a largepercentage of AI projects right
now are failing.
A lot of them get to asuccessful proof of concept, but
they fail to actually make itinto production or to produce
the results that are requiring.
The two areas that we see wherecompanies are struggling one is
(17:00):
data quality.
We harped on that earlier inthe discussion.
The data is key.
It doesn't matter how good yourmodel is if your data is poor.
Ai can't make up poor qualitydata.
So that data strategy and dataquality is important part of the
(17:22):
process, and that's why,whenever we talk to our clients
about AI, we're also talking tothem about the fundamentals of
data science.
The other aspect is just theoperational nature, and again
you may not initially thinkabout that when you're thinking
about AI technology, but reallydesigning an operational model
(17:48):
for how to get to production andhow to ensure a responsible AI
systems.
John Kundtz (17:54):
Excellent when I read your article, and there are
so many other topics and thingswe just don't have time to
cover today which I certainlywould love to talk about.
I think other people would wantto hear about as well.
So I would actually love tohave you come back on the show
and talk more about those atsome point in time, if you're
open to it.
But in wrapping it up, I wantto thank you, Greg, for a great
(18:20):
interview.
One more question, and that'show can people learn more about
what we're talking about todayand how might they get started
in developing their AI strategy?
Greg Pruett (18:32):
You gave a plug already for my blog on LinkedIn.
I'd love to see feedback fromfolks on LinkedIn on that, and
Kendra published a lot of otherblogs and very useful
information to folks to helplearn about this and learn how
(18:56):
we can help.
John Kundtz (18:57):
Excellent, so we'll definitely include the link to
your blog post on LinkedIn inthe show notes.
Of course, I recommend peopleconnect directly with you as
well.
That way, they can also checkout your article.
Five Steps to Getting Startedwith AI.
So wrap this up, greg.
Thanks for joining us.
(19:17):
It's been a great discussionand super exciting topic, and I
just want to thank you forjoining us on this edition of
the Cloud Collective Podcast.
Have a great day, everybody.
Thanks.
Greg Pruett (19:32):
John, Thanks everyone.
AI Insights with Greg Pruitt a five-step
blueprint for success.
Hi everybody, my name is JohnKuntz and welcome to this
special edition of the CloudCollective podcast.
(00:21):
In today's episode, I amexcited to welcome back Kindrel
Vice President, distinguishedengineer, multiple patent holder
and infrastructure cloudarchitect, greg Pruitt, as he
shares his valuable advice onhow to get started with
artificial intelligence.
If you are looking for help toframe your thinking around AI in
(00:42):
your AI projects, then you havecome to the right place.
I want to welcome back GregPruitt.
Hi, john, thanks for having meback on the show.
Hey, it's great to see youagain.
For those that weren't aroundor would like a refresh, why
don't you share a little bitabout your background with our
listeners and just feel free tostart anywhere you want?
Greg Pruett (01:07):
Thanks, john, I appreciate it.
My background is actuallyprimarily in system design.
I spent many years designingsystems for IBM and Lenovo, so I
have a deep engineeringbackground as well as a software
engineering background.
More recently, I've been partof Kindrel working on services
(01:33):
and helping our customers toadopt new technology, so I'm
part of the CTO office here inKindrel and recently doing some
very exciting work withartificial intelligence.
John Kundtz (01:46):
That's spectacular.
I'll tell you what.
Every time I get to talk to anyof our distinguished engineers,
it's always a fascinating topicand always learn a lot.
You can't go anywhere todaywithout having something in the
news about artificialintelligence, or AI.
There's a ton of hype around it, particularly around gen AI or
(02:07):
generative AI or chat GPT, butAI has been around a pretty long
time and before this hypearound generative AI came along,
there was predictive AI, whichis sometimes referred to as
traditional AI.
I wonder if you could justspend a moment, greg, explaining
the differences and maybe givesome examples of each.
Greg Pruett (02:29):
That's a great point, John.
Artificial intelligence isaround in a lot of different
forms, and generative AI is justone field of artificial
intelligence.
But there are numerous otherplaces where artificial
intelligence has made an impacton our lives.
You think about naturallanguage processing, Think about
(02:55):
your home devices, your Google,your Alexa, your Siri.
Those are devices that canunderstand speech and respond to
speech requests, typicallyusing something like a recurrent
(03:16):
neural network in R&M.
Also tremendous advances in thearea of computer vision over
the years typicallyconvolutional neural network,
CNNs that are very good atrecognizing objects or being
(03:37):
able to recognize objects invideo, used in transportation,
for self-driving vehicles, usedin retail for self-checkout or
loss prevention.
A lot of these, what you callmore traditional AI technologies
(04:01):
, are also maturing and becomingmuch more widely used in
production environments.
It's not just about looking atgenerative AI.
We're helping our clients withall types of AI.
Now, generative AI, like youwere saying, is quite different.
(04:22):
While it's based on pre-traineddata, generative AI actually
has this unique ability togenerate or create new content.
It provides some I'll sayrandomization to the pre-trained
data and allows it to createsummaries of documents, write
(04:49):
Q&As based on a large volume ofdata that actually create blogs
or even presentations.
Now we're seeing some excitingnew areas of generative AI
creating pictures and evengenerating videos.
John Kundtz (05:08):
Greg, many companies are reading about AI,
especially large enterprises.
They're trying to figure outhow to leverage some of these
technologies to innovate, toimprove productivity, to help
their decision-making, gaincompetitive advantage, reduce
time to market, stuff like that.
(05:30):
But I think a lot of newtechnologies people struggle on
how do you get started?
Greg Pruett (05:38):
With AI.
I think there's multiple stepsto it.
Maybe I can organize thatanswer into a few steps.
First, like you were saying,there are lots of ways that you
could apply AI, but we reallyencourage our customers to do a
(06:03):
very structured assessment sothat they can look at different
business needs, differentdesired outcomes, and go through
and score what kind of returnon investment they could get out
of these different scenarios.
I really think it starts withbusiness needs and how you can
(06:29):
achieve business differentiation.
Then, I would say, probably thesecond most important thing is
thinking about true data andyour data strategy.
Ai can do amazing things interms of consolidating data or
(06:52):
producing insights out of dataor making predictions from data,
but the whole system is only asgood as the data.
So most of us need to spendconsiderable time thinking about
data science and thinking aboutwhat kind of data quality, data
(07:15):
cleanup, data reliability needsto be implemented, as the data
sources are certainly veryimportant, but along with that
and last time, of course, john,we talked about sovereign cloud
Another very important thingthat we're seeing more and more
(07:36):
in the news these days is, youknow, making sure you deal with
anonymizing data, keeping dataprivate and ensuring fairness
and avoiding bias and all ofthose things.
John Kundtz (07:57):
So there's a lot of aspects to your data strategy
in terms of making sure it's agood data set, it's an inclusive
data set, it's a representativedata set, and so that's another
very important aspect of, Iwould say, preparing and that's
probably a key step, right, ifyou think about it, if you think
(08:22):
about these large languagemodels, and you don't want to
train them on somebody else'sdata, and you certainly don't
want to expose your data tosomebody outside of your
organization, particularly ifyou're trying to build a
competitive advantage or dosomething innovative.
It's the garbage in garbage.
(08:42):
Have to sort up your data, yourstrategy, where it is, who has
access to it, who doesn't haveaccess to it, before you can
actually, I think, really dosomething unique within your
organization, particularly alarge enterprise.
Does that make sense?
Greg Pruett (09:01):
Yeah and John, I joke with people sometimes that
the biggest winners in AI mayactually be the lawyers.
We're starting to see more andmore public lawsuits against AI
companies, either for trainingdata that may have included
(09:24):
trademark material, or due tobias and fairness or openness.
We're seeing various types oflawsuits now, so that data
strategy is really key.
What are your data sources?
What are your privacy controls,your responsibility controls?
(09:47):
Cool, the number three.
In the first two you notice Ididn't really mention any AI
terms like creating models orany of that.
I was just talking about basicsbusiness needs, return on
investment and data sources andprivacy.
(10:09):
I would say after that, you dowant to think about what type of
AI, like you're asking, isgenerative AI correct or are
there other AI technologies thatcan help solve that problem?
One thing I normally recommendis to think carefully about
(10:32):
adopting what I call an AIsoftware platform.
There's a lot of very good AIplatforms emerging.
Of course, there's the GoogleVertex platform, the NVIDIA AI
Enterprise platform.
There's Red Hat OpenShift AIplatform.
(10:52):
There's a number of very goodsoftware platforms that are
emerging that can really helpyou adopt AI more quickly in
terms of using it, not inventingit.
These AI platforms can help youby providing you pre-trained
models.
(11:13):
Maybe you don't even need to gothrough the training step, or
maybe you do, but maybe you canstart with a pre-trained model
that's pretty good and then dosome customizing or tuning on
that model with your ownspecific date.
Then after that, after youunderstand and pick an AI
(11:36):
software platform, then it'swhen I would start thinking
about infrastructure.
Where do you want to run this?
Where do you want to run thisin terms of a production
environment?
Do you want to run it in publiccloud?
Do you have data sovereigntyrequirements that make you want
to run it in private cloud?
(11:56):
All of those are possible.
You're going to have to makedecisions, choosing a software
platform and choosing a privateor public infrastructure for
hosting your production.
Ai, I think, or steps three.
John Kundtz (12:15):
That makes sense, like she's describing.
Your first two steps arebasically I would call it basic
business strategy blocking andtackling.
Now you're ready to put thefoundation.
Now you're ready to build thewalls.
You've got to pick yourplatform and then figure out
where you want to store yourdata and run the software.
(12:37):
Now we've got the first fourdown.
I believe there's one more stepthat you recommend.
Greg Pruett (12:47):
So for the fifth step, I think you really have to
think about the operationalmodel, the process for running a
production AI environment, andmaybe this isn't as intuitive
Like any other softwareapplication.
(13:07):
You will need to think aboutthe lifecycle of that
application.
How do you update the contentin the model?
Maybe if you have a generativeAI model that's trained on all
the user manuals for automobiles, what if a new generation of
(13:29):
automobiles comes out, a newmodel years?
You need to be able to updatethe model and add more data.
Whenever you update a model,though, there's a number of
things that should be done.
We talked about, with the datastrategy, making sure that your
data is clean and accurate andrepresentative, but also your
(13:56):
model needs to be tested to seeif there's bias, to see if it
produces bad results, and soanytime you retrain, you want to
retest and look at thoseresponsible AI processes to
(14:18):
ensure that what you're puttingout is of high quality.
John Kundtz (14:26):
Makes sense.
If you look at some of thegenerative AI companies like
OpenAI or Claude from Anthropicsif you just pick on OpenAI
GPT-4, it was around for almostsix or eight months before they
let it loose into the world.
I believe, from what I've read,they spent a lot of time on
(14:48):
security, on accuracy, onprivacy and bias and all those
things you just talked about.
Hit the market like with a bang.
I think you're right.
The operational processes aregoing to be your guardrails.
How is it used?
We learned of anything is youcan't just take everything right
(15:09):
out the gate as gospel truthright, at least from my
experience with generative AI.
It's another smart person inthe room I normally look forward
to that fifth step, as isModelOps or Machine LearningOps,
modelops Basically the fivesteps and, by the way, this is
based on your blog posts thatyou wrote and will obviously
(15:31):
include the link in the shownotes to it, the summary.
You analyze your business needs, your desired outcomes, the
requirements.
You define your data strategy,pick an AI software platform and
then you select how you'regoing to run the infrastructure
public cloud versus privatecloud and then you establish
(15:52):
your ModelOps processes.
That is, I think, excellent andvery valuable information.
Any concluding thoughts orparting words of wisdom on this
topic?
Greg Pruett (16:07):
I guess I'll say two things.
One I hope I didn't make itsound too daunting.
I think there's a fairly simpleformula and we have a process
that we're doing with ourclients that's repeatable and
ensuring successful AI projects.
The second thing, though, I'llsay is that there's some studies
(16:31):
out there that say that a largepercentage of AI projects right
now are failing.
A lot of them get to asuccessful proof of concept, but
they fail to actually make itinto production or to produce
the results that are requiring.
The two areas that we see wherecompanies are struggling one is
(17:00):
data quality.
We harped on that earlier inthe discussion.
The data is key.
It doesn't matter how good yourmodel is if your data is poor.
Ai can't make up poor qualitydata.
So that data strategy and dataquality is important part of the
(17:22):
process, and that's why,whenever we talk to our clients
about AI, we're also talking tothem about the fundamentals of
data science.
The other aspect is just theoperational nature, and again
you may not initially thinkabout that when you're thinking
about AI technology, but reallydesigning an operational model
(17:48):
for how to get to production andhow to ensure a responsible AI
systems.
John Kundtz (17:54):
Excellent when I read your article, and there are
so many other topics and thingswe just don't have time to
cover today which I certainlywould love to talk about.
I think other people would wantto hear about as well.
So I would actually love tohave you come back on the show
and talk more about those atsome point in time, if you're
open to it.
But in wrapping it up, I wantto thank you, Greg, for a great
(18:20):
interview.
One more question, and that'show can people learn more about
what we're talking about todayand how might they get started
in developing their AI strategy?
Greg Pruett (18:32):
You gave a plug already for my blog on LinkedIn.
I'd love to see feedback fromfolks on LinkedIn on that, and
Kendra published a lot of otherblogs and very useful
information to folks to helplearn about this and learn how
(18:56):
we can help.
John Kundtz (18:57):
Excellent, so we'll definitely include the link to
your blog post on LinkedIn inthe show notes.
Of course, I recommend peopleconnect directly with you as
well.
That way, they can also checkout your article.
Five Steps to Getting Startedwith AI.
So wrap this up, greg.
Thanks for joining us.
(19:17):
It's been a great discussionand super exciting topic, and I
just want to thank you forjoining us on this edition of
the Cloud Collective Podcast.
Have a great day, everybody.
Thanks.
Greg Pruett (19:32):
John, Thanks everyone.
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